AI AND SUSTAINABILITY - 2024/5

Module Overview

Recently, Artificial Intelligence (AI) has been playing a key role in the research and development of scientific and technological breakthroughs in many disciplines to solve real world problems, providing new foundations and steppingstones to foster more advances and solutions. In this context, AI has a great potential to play a transformative role in helping to achieve the United Nations Sustainable Development Goals (UNSDG), by providing new insights, enabling more efficient use of resources, and supporting a better understanding of complex systems that underpin the dynamics of people's lives and the planet's environment. Therefore, the purpose of this module is to present the key concepts with practical applications related to the development of more sustainable AI techniques (e.g. model, data and energy efficiency, bias and unfairness identification and mitigation, trustworthy AI, physics-informed neural networks etc.), and AI solutions to support UNSDGs (e.g. clean air, clean energy, clean water, waste management, smart manufacturing etc.).

Module provider

Computer Science and Electronic Eng

Module Leader

SPERANDIO NASCIMENTO Erick (CS & EE)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

Students are advised to possess good programming skills, ideally in Python.

Module content

Introduction to AI and Sustainability

AI core concepts recap

AI for time series processing Trustworthy

AI Model and data efficiency

Physics-Informed Neural Networks

Advanced topics in AI and Sustainability

Practical applications in AI and Sustainability

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate:

Ability to understand and implement the key concepts and core fundamental knowledge in sustainability of AI and AI for sustainability;

Ability to understand the needs required to build appropriate solutions for a range of sustainability challenges and problems using AI techniques, either in the sustainability of AI or AI for sustainability;

Ability to develop, evaluate, experiment, demonstrate and apply appropriate AI techniques to tackle real-world sustainability challenges, generating suitable and defensible results.

Thus, the summative assessment for this module consists of:

• Individual Coursework: a report along with the corresponding code (in Jupyter Notebook format) developed to solve a real-world problem either in the sustainability of AI or AI for sustainability. This addresses LOs 1,2,3, and 4.

Formative assessment:

For the module, students will receive formative assessment/feedback in the following ways. During lectures, by question and answer sessions and group discussions During labs, by question and answer sessions and group discussions using worked examples By means of unassessed tutorial problems (with answers/model solutions) Via the marking of the assignment, both electronic file submissions, codes and written report Feedback: Students will be taught weekly through lectures and guided to work on weekly practical tasks through lab exercises. The corresponding solutions will provide feedback on understanding and practicing. Lectures, labs, tutorials, independent learning, and feedback will then support the coursework. Individual feedback on the coursework will be given to the student.

Module aims

• This module aims to provide an understanding of what AI and Sustainability means i.e. the aspects of the sustainability of AI, and AI for sustainability, time series processing with AI, trustworthy AI, model and data efficiency techniques, physics-informed neural networks, along with some advanced topics in AI that play a key role in the sustainable development, such as foundation models and diffusion models applied to sustainability challenges.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

This module aims to present an introductory course on the core concepts, background theory and key fundamental aspects of the sustainability of AI and AI for sustainability i.e. AI and Sustainability through lectures designed to deliver modern theorectical content, along with lab activities to present to the student practical applications of what was learnt in the lectures.

The lectures will be presented using PowerPoint presentations containing the teaching materials, along with annotations in the whiteboard. Interactions between the lecturer and the students will be carried out to stimulate and foster the learning process, which will be based on some question-and-answering sessions. The labs will be designed in Python, aiming at providing further technical depth of the content delivered to the students, with practical examples of real-world situations where students would need to apply these concepts.

The learning and teaching methods include:

Lectures

Labs

Tutorials

Class discussion integrated within lectures, labs and tutorials

Designed real-world problems to be used as case studies during lectures and labs

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: EEEM073

Other information

The School of Computer Science and Electronic Engineering is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas: Digital capabilities: The advanced topics in AI and Sustainability taught in this module will provide students skills in coding and other computer science techniques that are fundamental in developing and deploying more sustainable AI solutions and solving sustainability challenges using AI techniques. Employability: This module provides transferable and underpinning skills in advanced programming, AI, sustainability, software and data processing that are important in solving many real-world problems in AI and sustainability. This includes skills in developing AI solutions that are environmentally and socially sustainable, and that aim to solve sustainability challenges that contribute to achieving UNSDGs. Those skills are highly appreciated by employers in many disciplines of knowledge, which are even more important for those companies working towards achieving Industry 5.0 and Society 5.0 standards. Global and cultural capabilities: AI and sustainability are global challenges that are of great interest to industries, companies, governments and societies worldwide. This module provides a unique opportunity to delve into the intersection of these two areas and develop hard and soft skills that can be used not only regionally but also globally, contributing to raise awareness on the importance of developing more sustainable AI solutions, that can also be applied to solve sustainability problems. Resourcefulness and Resilience: This module allows students to develop skills in methods they have learned in lecture and labs materials, so that they can be ready to reason about and solve new unseen real-world problems in AI and sustainability. Sustainability: This module is especially linked to Sustainability as it is focused on bringing the core knowledge and developing the skills necessary to develop more sustainable AI solutions, and how to develop them to solve sustainability challenges to achieve UNSDGs.

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2024/5 academic year.